CN103390169A - Sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects - Google Patents
Sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects Download PDFInfo
- Publication number
- CN103390169A CN103390169A CN201310307332XA CN201310307332A CN103390169A CN 103390169 A CN103390169 A CN 103390169A CN 201310307332X A CN201310307332X A CN 201310307332XA CN 201310307332 A CN201310307332 A CN 201310307332A CN 103390169 A CN103390169 A CN 103390169A
- Authority
- CN
- China
- Prior art keywords
- road surface
- data
- cloud data
- point
- cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Length Measuring Devices By Optical Means (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects. The method mainly comprises two steps as follows: original vehicle-mounted laser scanning point cloud data are subjected to road surface filtering in combination of vehicle tracking data, and point cloud data of a road surface are separated from point cloud data of other ground objects; and the obtained point cloud data of other ground objects are further identified to be different ground object types. In the road surface filtering algorithm, the vehicle tracking data are combined, and a histogram statistic and analytical method is introduced, so that the self-adaption degree, the automation degree and the accuracy of the filtering algorithm of the road surface in an urban environment are improved greatly; and in the sorting algorithm of other ground objects, a point cloud feature image sorting method based on the maximum elevation value is improved, mistakenly sorted point cloud data are corrected with a cellular automaton technology, and a new thought and method are provided for the sorting algorithms of vehicle-mounted laser scanning point cloud data.
Description
Technical field
The present invention relates to the mobile lidar technical field, relate in particular to the sorting technique of Vehicle-borne Laser Scanning cloud data.
Background technology
20 century 70s, Nasa (NASA) has researched and developed lidar measurement technology (LIDAR, Light Detection And Ranging), for obtaining spatial information, provides a kind of brand-new technological means.To the eighties in 20th century, Stuttgart, Germany university is combined laser radar technique with timely positioning and orientation system, use aircraft as carrier, successfully researches and develops airborne laser radar measuring system (ALSS, Airborne Laser Mapping System).In late 1980s, the vehicle of using is succeeded in developing as the vehicle-mounted mobile measuring system (MMS, Mobile Mapping System) of carrying platform.This technology provides a kind of reliable, flexible, efficient mode, has realized the high-precision three-dimensional data of the relevant atural object in Quick Acquisition road and road both sides.Yet in fact original Vehicle-borne Laser Scanning cloud data is exactly the set of rambling magnanimity three-dimensional coordinate point, has comprised the various atural objects such as road, buildings, trees, vertical rod, and the data scale of construction is large.Therefore, the Vehicle-borne Laser Scanning cloud data is further processed or three-dimensional modeling before,, if it can be classified accurately, will greatly improve efficiency and precision that follow-up data is processed.
Compared to the Vehicle-borne Laser Scanning cloud data, more ripe for the Research on classifying method of airborne laser scanning cloud data.200910272643.0), " a kind of fast filtering method of airborne laser cloud data " (application number: 201110337099.0), the intelligent filtering method of cloud data " plant airborne laser " (application number: 201210350254.7), appeal three patents of invention and all proposed corresponding filtering method and sorting technique for the airborne laser cloud data for example: " a kind of automatic classification method of airborne laser radar point cloud data " (application number:.But because operating type is different, Vehicle-borne Laser Scanning cloud data and airborne laser scan differing greatly of cloud data, and the sorting technique that therefore airborne laser can't be scanned cloud data directly is used on the Vehicle-borne Laser Scanning cloud data.
In recent years, domestic and international many scholars had successively proposed some sorting techniques for the Vehicle-borne Laser Scanning cloud data.But the self-adaptation degree of these methods is lower, and the common manually more threshold value of setting that needs, can't realize efficiently, classify accurately for the Vehicle-borne Laser Scanning cloud data in the complex environment of city.
Summary of the invention
The object of the invention is to solve existing problem in prior art, wheelpath data in conjunction with the Vehicle-borne Laser Scanning system, and introduce the technology such as statistics with histogram analysis, cellular automaton, to improve automaticity, efficiency and the degree of accuracy of current Vehicle-borne Laser Scanning cloud data sorting technique.
Technical scheme provided by the present invention comprises a kind of city terrain classification method of Vehicle-borne Laser Scanning cloud data, comprises following steps:
Step 1,, in conjunction with the wheelpath data, carry out road surface filtering to original vehicle-mounted Point Cloud of Laser Scanner, and road surface cloud data and other culture point cloud data separating are opened, and comprises following substep,
Step 1.1,, according to the wheelpath data, follow the wheel paths direction, use each wheelpath node as central point, build the rectangular filter window, the data that drop in the rectangular filter window are carried out statistics with histogram Analysis deterrmination road surface threshold range, obtain preliminary road surface cloud data;
Step 1.2, the resulting preliminary road surface cloud data of step 1.1 is analyzed from distance and two dimensions of angle, the mode of according to sweep trace, arranging arranges, the sweep trace numbering in the preliminary road surface cloud data of mark under each road surface data point;
Step 1.3, resulting road surface cloud data after step 1.2 sweep trace arranges is pursued the sweep trace analysis according to the sweep trace number order, comprise every sweep trace, calculate successively each road surface data point in this sweep trace and the difference of elevation of adjacent two road surface data points before and after it,, if two difference of elevation all exceed default difference of elevation threshold value, this road surface data point is rejected as assorted point; After picking impurity point from the resulting preliminary road surface cloud data of step 1.1, the road surface data point that keeps is as final road surface cloud data, reject final road surface cloud data from original vehicle-mounted Point Cloud of Laser Scanner after, obtain other atural object cloud datas;
Step 2.1, use each wheelpath node as central point, according to the default border circular areas that builds, drop on road surface cloud data in border circular areas according to step 1.3 acquired results search, calculate successively the dispersed elevation value of all road surface data points in each border circular areas and as the road surface height value of corresponding line wheel paths Nodes;
Step 2.2, traversal calculate each data point in other atural object cloud datas respectively with the horizontal range of all wheelpath nodes, choose the wheelpath node nearest with current institute ergodic data point, near the road surface height value height value of the current data point that travels through and step 2.1 gained corresponding line wheel paths node is poor, obtain in other atural object cloud datas each data point with respect near the relative altitude value of road surface;
Step 2.3, according to default sampling interval, build surface level rectangle rule graticule mesh, will be after step 2.2 elevation correction resulting other culture point cloud data projections in surface level rectangle rule graticule mesh, statistics drops on the maximum elevation value of all data points in each grid unit and, as the eigenwert of this grid unit, generates a some cloud characteristic image;
Step 2.4, carry out preliminary classification by the pre-set categories threshold value, obtains the numbering of the affiliated classification of each grid unit correspondence, forms other atural object preliminary classification results;
Step 2.5,, to through resulting other atural object preliminary classification results of step 2.4, use cellular automaton to carry out the differentiation of preset times, obtains other final terrain classification results.
And step 1.1 comprises following substep,
Step 1.1.1,, according to the scope that original vehicle-mounted Point Cloud of Laser Scanner covers, build two-dimensional space regular grid index, calculates the affiliated grid unit numbering of each data point and generating indexes file;
Step 1.1.2, traversal wheelpath node, build the rectangular filter window successively, and search drops on all data points in the rectangular filter window according to index file;
Step 1.1.3, carry out the statistics with histogram analysis to the height value of all data points in the resulting rectangular filter window of step 1.1.2, and statistical is as follows,
Use the elevation at minimum strong point in the current rectangle filter window as starting point, according to pre-set interval length, carry out from low to high subregion, establish be divided into n interval, statistics drops on counting in each interval successively, and successively n interval to be numbered from low to high be 0,1,2 ..., n-1; Pick out 5 the maximum intervals of counting, and determine the corresponding interval numbering in these 5 intervals;
These 5 intervals number are arranged according to order from small to large, centered by maximum interval MaxBin that counts, make progress respectively, 4 intervals of traversal residue downwards, pick out in 4 intervals of residue and MaxBin adjacent interval successively, form a continuum section;
Check in the continuum section, whether the smallest interval numbering is 0,
If be 0, with this continuum section as the road surface threshold range, the data point that falls in this continuum section is regarded as the road surface data point, obtain preliminary road surface cloud data;
If be not 0, with this continuum section and lower than all segments of this continuum section as the road surface threshold range, all regard as the road surface data point with the data point in this continuum section and lower than all data points of this continuum section, obtain preliminary road surface cloud data.
And, in step 2.5, the structure rule of cellular automaton is, cellular is the grid unit after step 2.4 preliminary classification, and the state value of each cellular is the classification numbering of grid unit, and the cellular space is the set that the possessive case net unit after step 2.4 preliminary classification forms;
The each differentiation travels through 1 time according to the cellular automaton rule that sets cellulars all in the cellular space, and described cellular automaton rule comprises following substep,
Step a, whether the state value S that differentiates current cellular is 0, if be 0 do not carry out follow-up differentiation and keep this state value, if be not 0 continue subsequent step b;
Step b, the number sum of all state values in the Moore neighbours of the current cellular of statistics
TypeIf there is the number sum of certain state value k
kGreater than 4, the state value of current cellular is changed to k and end to the differentiation of this cellular, if there is no continue subsequent step c;
Step c, in the Moore neighbours of the current cellular of statistics, all state values obtain maximum rating value S
maxIf, S
maxThe state value of current cellular is changed to S greatly than current cellular state value S
max, otherwise remain unchanged.
Technical scheme provided by the invention combines road surface filtering and other terrain classifications.In the road surface filtering algorithm, the present invention is in conjunction with the wheelpath data, and introducing statistics with histogram analytical approach, has greatly improved self-adaptation degree, automaticity and the degree of accuracy of road surface filtering algorithm in the urban environment; In other terrain classification algorithms, the present invention has improved the some cloud characteristic image classification based on the maximum elevation value, and the cloud data that utilizes the correction of cellular automaton technology to be divided by mistake, for the classification of Vehicle-borne Laser Scanning cloud data provides a kind of new thinking and method.
Description of drawings
Fig. 1 is the overall flow figure of the embodiment of the present invention;
Fig. 2 is the road surface filtering process flow diagram of the embodiment of the present invention;
Fig. 3 is that the road surface rectangular filter window of the embodiment of the present invention builds schematic diagram;
Fig. 4 is the filtering of the moving window based on the wheelpath data process flow diagram of the embodiment of the present invention;
Fig. 5 is other terrain classification process flow diagrams of the embodiment of the present invention;
Fig. 6 is other atural object preliminary classification process flow diagrams based on a cloud characteristic image method of the embodiment of the present invention;
Fig. 7 is the Moore type neighbours model of the embodiment of the present invention, i.e. eight neighborhood neighbours models.
Specific implementation method
Technical solution of the present invention can adopt computer software technology to realize automatic operational scheme.Understand and implement the present invention for the ease of those of ordinary skills, the present invention is described in further detail below in conjunction with drawings and Examples.
With reference to Fig. 1, the embodiment of the present invention provides a kind of city terrain classification method of Vehicle-borne Laser Scanning cloud data, comprising:
Step 1,, in conjunction with the wheelpath data, carry out road surface filtering to original vehicle-mounted Point Cloud of Laser Scanner, and road surface cloud data and other culture point cloud data separating are opened;
Be further used as preferred embodiment, with reference to Fig. 2, described step 1 comprises:
Step 1.1, mobile rectangular filter window filtering algorithm based on wheelpath: according to the wheelpath data, follow the wheel paths direction, use each wheelpath node as central point, build the rectangular filter window, carry out the statistics with histogram analysis to dropping on these data that move in the rectangular filter window,, to determine the road surface threshold range, obtain preliminary road surface cloud data.
With reference to Fig. 3, the construction method of embodiment rectangular filter window is specially:
Follow the wheel paths direction, use each wheelpath node as central point, build the rectangular filter window., for each wheelpath node O, build this wheelpath node O and arrive the vector of adjacent lines wheel paths node A thereafter
Can obtain vertical
Vector
The adjacent both sides of rectangular filter window are parallel to respectively
Direction and
Direction.In the rectangular filter window, be parallel to
The length of side length l ength of direction is
Be parallel to
The length of side length width of direction needs only greater than the road surface width, respectively extend to as about figure middle rolling car track node O-width/2 ,+width/2.During concrete enforcement, those skilled in the art are width value default according to concrete road conditions voluntarily, and in the present embodiment, width gets 40 meters.
Be further used as preferred embodiment, with reference to Fig. 4, described step 1.1 comprises:
Step 1.1.1,, according to the scope that original vehicle-mounted Point Cloud of Laser Scanner covers, build two-dimensional space regular grid index, and it is 1/2nd of rectangular filter window length of side width that the graticule mesh length of side is established in suggestion, namely 20 meters.Travel through original vehicle-mounted Point Cloud of Laser Scanner, calculate the affiliated grid unit numbering i of each data point and j, the generating indexes file.Wherein, i represents horizontal direction grid unit numbering, and j represents vertical direction grid unit numbering, and the span of i and j depends on the scope that original vehicle-mounted Point Cloud of Laser Scanner covers.In the search rectangular filter window during data point, use this index file can greatly promote operation efficiency in step 1.1.2.
Step 1.1.2, traversal wheelpath node, build the rectangular filter window successively, and search drops on all data points in the rectangular filter window.Concrete searching method is:
At first, determine wheelpath node route
kGrid unit grid in step 1.1.1 in constructed graticule mesh space
ijSupposing has p with the corresponding wheelpath node of original vehicle-mounted Point Cloud of Laser Scanner, and the span of k is 1,2,3 ..., p, k represent the numbering of the wheelpath node of current computing.
Then, in order to accelerate search efficiency, the graticule mesh file that generates in read step 1.1.1, search grid unit grid
ijAnd grid
ijEight neighborhood spaces, with reference to Fig. 7, i.e. grid
(i-1) (j-1), grid
(i-1) j, grid
(i-1) (j+1), grid
I (j-1), grid
I (j+1), grid
(i+1) (j-1), grid
(i+1) j, grid
(i+1) (j+1), to the data point in above-mentioned nine grid units, calculate successively each data point and arrive
With
Apart from d
OAAnd d
OBIf, d
OALess than width and d
OBLess than length, this point namely drops in the rectangular filter window.Traversal is completed, and can determine to drop on all data points in the rectangular filter window.
Step 1.1.3, carry out the statistics with histogram analysis to the height value of all data points in the resulting rectangular filter window of step 1.1.2.Concrete statistical is: use the elevation at minimum strong point in the current rectangle filter window as starting point, according to pre-set interval length, carry out subregion, those skilled in the art can preset value voluntarily.In embodiment 0.1 meter as burst length, establish total n interval.Statistics drops on counting in each interval successively, until cover the maximum elevation value in this rectangular filter window.Successively n interval being numbered from low to high is 0,1,2 ..., n-1.Pick out 5 the maximum intervals of counting, and determine the corresponding interval numbering in these 5 intervals.These 5 intervals number are arranged according to order from small to large, centered by maximum interval MaxBin that counts, make progress respectively, travel through 4 intervals of residue downwards, pick out in 4 intervals of residue and MaxBin adjacent interval successively, namely, with the adjacent interval of maximum interval elevations of counting, form a continuum section.Such as, 5 the maximum intervals of counting are followed successively by 0,1,2,3,5 from small to large according to numbering, and wherein counting interval numbering MaxBin maximum is 2, upwards travels through 1,0,0,1,2 adjacently successively, travels through 3,5,2,3 adjacently downwards, but 3,5 is non-conterminous; 0,1,3 be and MaxBin adjacent interval successively.After having selected, check whether the smallest interval numbering is 0 in this continuum section: if be 0, with this continuum section as the road surface threshold range, the data point that falls in this continuum section is namely regarded as road surface data point (being the road millet cake); If be not 0, with this continuum section and lower than all segments of this continuum section as the road surface threshold range, namely except the data point with in this continuum section, regard as the road surface data point, to all regard as the road surface data point lower than all data points of this continuum section, obtain preliminary road surface cloud data.
Step 1.2, the resulting preliminary road surface cloud data of step 1.1 is analyzed from distance and two dimensions of angle, its mode of according to sweep trace, arranging is arranged the sweep trace numbering in the preliminary road surface cloud data of mark under each road surface data point.
In the file of the vehicle-mounted Point Cloud of Laser Scanner of storage, the some record is generally arranged according to Vehicle-borne Laser Scanning system acquisition time order and function.Read successively the some record in the cloud data file, calculate respectively distance and angle.Here, " distance " refer to calculate respectively in preliminary road surface cloud data each point and consecutive point thereafter apart from distance; " angle " refers to each point in preliminary road surface cloud data and its front and back two consecutive point are formed respectively vector, calculates the angle angle of this two vector.
Difference setpoint distance threshold value and angle threshold, if certain a bit meets distance simultaneously greater than setting distance threshold, angle, less than setting angle threshold, regards as this some the end points of sweep trace.During concrete enforcement, those skilled in the art can be voluntarily according to concrete road conditions predeterminable range threshold value and angle threshold, and the present embodiment middle distance threshold value is made as 5 meters, and angle threshold is made as 90 degree.
Step 1.3,, to resulting road surface cloud data after step 1.2 sweep trace arranges, pursue the sweep trace analysis according to the sweep trace number order.To every sweep trace, calculate successively each road surface data point in this road surface sweep trace and the difference of elevation of adjacent two road surface data points before and after it, and setting threshold,, if two difference of elevation all exceed threshold value, assert that it is " assorted point ", weeds out it.During concrete enforcement, those skilled in the art can preset the difference of elevation threshold value voluntarily, and in the present embodiment, the difference of elevation threshold value is made as 0.2 meter.For the sweep trace end points, only need this end points of judgement to be adjacent the difference of elevation of a bit,, if greater than 0.2 meter, assert that namely this end points is " assorted point ", and it is weeded out.After picking impurity point from the resulting preliminary road surface cloud data of step 1.1, the road surface data point of reservation is final road surface cloud data.Reject final road surface cloud data from original vehicle-mounted Point Cloud of Laser Scanner after, obtain other atural object cloud datas.
Be further used as preferred embodiment, with reference to Fig. 5, described step 2 comprises:
Step 2.1, with reference to Fig. 6, use each wheelpath node as central point, a less radius is set, build border circular areas, search drops on the road surface cloud data in border circular areas according to step 1.3 acquired results, calculates successively the dispersed elevation value of all road surface data points in each border circular areas, with its height value of road surface as this wheelpath Nodes.During concrete enforcement, the radius value can be preset by those skilled in the art, and suggestion makes road surface data point number in the border circular areas that builds take each wheelpath node as the center of circle greater than 10 by value.In the present embodiment, the radius value is 1 meter.
Step 2.2, with reference to Fig. 6, traversal is resulting other atural object cloud datas after step 1 is rejected the road surface cloud data, calculate successively each data point in other atural object cloud datas respectively with the horizontal range of all wheelpath nodes.Through comparing, choose the wheelpath node nearest with current institute ergodic data point, near this wheelpath node that the height value of the current data point that travels through and step 2.1 are calculated, the road surface height value is poor, realize elevation correction, obtain in other atural object cloud datas each data point with respect near the relative altitude value of road surface it, with error in classification and the impact of avoiding road surface to rise and fall and brought.
Step 2.3, with reference to Fig. 6, build surface level rectangle rule graticule mesh according to default sampling interval, will be after step 2.2 elevation correction resulting other culture point cloud data projections in surface level rectangle rule graticule mesh, drop on the maximum elevation value of all data points in each grid unit with respect near the relative altitude Data-Statistics of road surface according to each data point in other atural object cloud datas, with its eigenwert as this grid unit, thereby generate some cloud characteristic image.Building surface level rectangle rule graticule mesh is prior art, and surface level namely refers to the XOY plane of earth coordinates, and it will not go into details in the present invention.During concrete enforcement, the suggestion sampling interval is got a less value,, such as 0.1 meter, to avoid the cloud data that will originally belong to different atural objects, is divided in same grid unit.Provide improved maximum height value method by step 2.1 to 2.3, generate a some cloud characteristic image.
Step 2.4, passing threshold are cut apart and are carried out other atural object preliminary classifications: with reference to Fig. 6, while specifically implementing, can preset for types of ground objects different in concrete urban environment its elevation threshold value, such as buildings, vertical rod, trees, and for each class atural object, be numbered, establish total t class atural object, represent with 0 the grid unit that does not have cloud data to distribute, be 0,1,2 according to from low to high order number consecutively,, t.While moving this step, the point cloud characteristic image that step 2.3 is generated is take each grid unit as unit, passing threshold is cut apart and is carried out preliminary classification, it is divided into buildings, vertical rod, trees and other atural object etc., and the numbering corresponding with classification under it is assigned to each grid unit, obtain other atural object preliminary classification results.
Step 2.5, to through resulting other atural object preliminary classification results of step 2.4, use principle of cellular automation to carry out the differentiation of certain number of times, the each differentiation travels through 1 time according to the cellular automaton rule that sets cellulars all in the cellular space, to revise the grid unit value of by mistake, being divided, improve the accuracy of classification results, reduction may arrange the improper impact that brings due to sampling interval in step 2.3.More independent each other for atural object, the less situation that adjoins each other, can carry out the differentiation of more number of times, to guarantee that differentiation fully.And for there being the more adjacent or situation of joining between atural object, cellular automaton develops number of times and can determine through overtesting: develop number of times very few, can cause develop insufficient; The differentiation number of times is too much, can cause and develop excessively.In the present embodiment, suggestion develops 50 times, obtains other final terrain classification results.
In the present embodiment, the structure rule of cellular automaton is:
(1) cellular: the grid unit after step 2.4 preliminary classification result, the state value of each cellular are the classification numbering of corresponding grid unit, and the span of cellular state value is determined type of ground objects numbering 0,1,2 in step 2.4 ..., t;
(2) cellular space: the possessive case net unit set after step 2.4 preliminary classification result;
(3) neighbours: Moore type neighbours model, i.e. eight neighborhood neighbours models;
In the present embodiment, the cellular automaton rule is specially:
Step a, whether the state value S that differentiates current cellular is 0, if be 0 do not carry out follow-up differentiation and keep this state value, if be not 0 continue subsequent step b;
Step b, the number sum of all state values in the Moore neighbours of the current cellular of statistics
TypeIf there is the number sum of certain state value k
kGreater than 4, the state value of current cellular is changed to k and end to the differentiation of this cellular, if there is no continue subsequent step c;
Step c, in the Moore neighbours of the current cellular of statistics, all state values obtain maximum rating value S
maxIf, S
maxThe state value of current cellular is changed to S greatly than current cellular state value S
max, otherwise remain unchanged.
Above-described specific embodiment, further describe technical scheme of the present invention, is only the specific embodiment of the invention case is described, but not in order to limit practical range of the present invention.Those of ordinary skill in the art complete under the spirit indicated without prejudice to the present invention and principle all equivalent deformations, replacement or modification, still be included in the scope of the claims in the present invention.
Claims (3)
1. the city terrain classification method of a Vehicle-borne Laser Scanning cloud data, is characterized in that, comprises following steps:
Step 1,, in conjunction with the wheelpath data, carry out road surface filtering to original vehicle-mounted Point Cloud of Laser Scanner, and road surface cloud data and other culture point cloud data separating are opened, and comprises following substep,
Step 1.1,, according to the wheelpath data, follow the wheel paths direction, use each wheelpath node as central point, build the rectangular filter window, the data that drop in the rectangular filter window are carried out statistics with histogram Analysis deterrmination road surface threshold range, obtain preliminary road surface cloud data;
Step 1.2, the resulting preliminary road surface cloud data of step 1.1 is analyzed from distance and two dimensions of angle, the mode of according to sweep trace, arranging arranges, the sweep trace numbering in the preliminary road surface cloud data of mark under each road surface data point;
Step 1.3, resulting road surface cloud data after step 1.2 sweep trace arranges is pursued the sweep trace analysis according to the sweep trace number order, comprise every sweep trace, calculate successively each road surface data point in this sweep trace and the difference of elevation of adjacent two road surface data points before and after it,, if two difference of elevation all exceed default difference of elevation threshold value, this road surface data point is rejected as assorted point; After picking impurity point from the resulting preliminary road surface cloud data of step 1.1, the road surface data point that keeps is as final road surface cloud data, reject final road surface cloud data from original vehicle-mounted Point Cloud of Laser Scanner after, obtain other atural object cloud datas;
Step 2, further be identified as different types of ground objects with other atural object cloud datas of step 1 gained, comprises following substep,
Step 2.1, use each wheelpath node as central point, according to the default border circular areas that builds, drop on road surface cloud data in border circular areas according to step 1.3 acquired results search, calculate successively the dispersed elevation value of all road surface data points in each border circular areas and as the road surface height value of corresponding line wheel paths Nodes;
Step 2.2, traversal calculate each data point in other atural object cloud datas respectively with the horizontal range of all wheelpath nodes, choose the wheelpath node nearest with current institute ergodic data point, near the road surface height value height value of the current data point that travels through and step 2.1 gained corresponding line wheel paths node is poor, obtain in other atural object cloud datas each data point with respect near the relative altitude value of road surface;
Step 2.3, according to default sampling interval, build surface level rectangle rule graticule mesh, will be after step 2.2 elevation correction resulting other culture point cloud data projections in surface level rectangle rule graticule mesh, statistics drops on the maximum elevation value of all data points in each grid unit and, as the eigenwert of this grid unit, generates a some cloud characteristic image;
Step 2.4, carry out preliminary classification by the pre-set categories threshold value, obtains the numbering of the affiliated classification of each grid unit correspondence, forms other atural object preliminary classification results;
Step 2.5,, to through resulting other atural object preliminary classification results of step 2.4, use cellular automaton to carry out the differentiation of preset times, obtains other final terrain classification results.
2. the city terrain classification method of Vehicle-borne Laser Scanning cloud data according to claim 1, it is characterized in that: step 1.1 comprises following substep,
Step 1.1.1,, according to the scope that original vehicle-mounted Point Cloud of Laser Scanner covers, build two-dimensional space regular grid index, calculates the affiliated grid unit numbering of each data point and generating indexes file;
Step 1.1.2, traversal wheelpath node, build the rectangular filter window successively, and search drops on all data points in the rectangular filter window according to index file;
Step 1.1.3, carry out the statistics with histogram analysis to the height value of all data points in the resulting rectangular filter window of step 1.1.2, and statistical is as follows,
Use the elevation at minimum strong point in the current rectangle filter window as starting point, according to pre-set interval length, carry out from low to high subregion, establish be divided into n interval, statistics drops on counting in each interval successively, and successively n interval to be numbered from low to high be 0,1,2 ..., n-1; Pick out 5 the maximum intervals of counting, and determine the corresponding interval numbering in these 5 intervals;
These 5 intervals number are arranged according to order from small to large, centered by maximum interval MaxBin that counts, make progress respectively, 4 intervals of traversal residue downwards, pick out in 4 intervals of residue and MaxBin adjacent interval successively, form a continuum section;
Check in the continuum section, whether the smallest interval numbering is 0,
If be 0, with this continuum section as the road surface threshold range, the data point that falls in this continuum section is regarded as the road surface data point, obtain preliminary road surface cloud data;
If be not 0, with this continuum section and lower than all segments of this continuum section as the road surface threshold range, all regard as the road surface data point with the data point in this continuum section and lower than all data points of this continuum section, obtain preliminary road surface cloud data.
3. the city terrain classification method of described Vehicle-borne Laser Scanning cloud data according to claim 1 and 2, it is characterized in that: in step 2.5, the structure rule of cellular automaton is, cellular is the grid unit after step 2.4 preliminary classification, the state value of each cellular is the classification numbering of grid unit, and the cellular space is the set that the possessive case net unit after step 2.4 preliminary classification forms;
The each differentiation travels through 1 time according to the cellular automaton rule that sets cellulars all in the cellular space, and described cellular automaton rule comprises following substep,
Step a, whether the state value S that differentiates current cellular is 0, if be 0 do not carry out follow-up differentiation and keep this state value, if be not 0 continue subsequent step b;
Step b, the number sum of all state values in the Moore neighbours of the current cellular of statistics
TypeIf there is the number sum of certain state value k
kGreater than 4, the state value of current cellular is changed to k and end to the differentiation of this cellular, if there is no continue subsequent step c;
Step c, in the Moore neighbours of the current cellular of statistics, all state values obtain maximum rating value S
maxIf, S
maxThe state value of current cellular is changed to S greatly than current cellular state value S
max, otherwise remain unchanged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310307332.XA CN103390169B (en) | 2013-07-19 | 2013-07-19 | A kind of city terrain classification method of Vehicle-borne Laser Scanning cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310307332.XA CN103390169B (en) | 2013-07-19 | 2013-07-19 | A kind of city terrain classification method of Vehicle-borne Laser Scanning cloud data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103390169A true CN103390169A (en) | 2013-11-13 |
| CN103390169B CN103390169B (en) | 2016-08-31 |
Family
ID=49534436
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310307332.XA Expired - Fee Related CN103390169B (en) | 2013-07-19 | 2013-07-19 | A kind of city terrain classification method of Vehicle-borne Laser Scanning cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103390169B (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103675818A (en) * | 2013-12-03 | 2014-03-26 | 中国科学院深圳先进技术研究院 | Method and system for measuring polarimetric synthetic aperture radar data ground object three-dimensional distance |
| CN103743383A (en) * | 2014-02-11 | 2014-04-23 | 天津市星际空间地理信息工程有限公司 | Automatic extraction method for road information based on point cloud |
| CN103778429A (en) * | 2014-01-24 | 2014-05-07 | 青岛秀山移动测量有限公司 | Method for automatically extracting road information in vehicle-mounted laser scanning point cloud |
| CN104463872A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Classification method based on vehicle-mounted LiDAR point cloud data |
| CN104596415A (en) * | 2014-12-29 | 2015-05-06 | 中国神华能源股份有限公司 | Method and device for determining lower edge of stack based on laser scanning single lines |
| CN105184852A (en) * | 2015-08-04 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Laser-point-cloud-based urban road identification method and apparatus |
| CN105550688A (en) * | 2015-12-04 | 2016-05-04 | 百度在线网络技术（北京）有限公司 | Method and device for point cloud data classification |
| CN106127153A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning cloud data |
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN103853840B (en) * | 2014-03-18 | 2017-05-03 | 中国矿业大学(北京) | Filter method of nonuniform unorganized-point cloud data |
| CN108241819A (en) * | 2016-12-23 | 2018-07-03 | 高德软件有限公司 | The recognition methods of pavement markers and device |
| CN108681525A (en) * | 2018-05-16 | 2018-10-19 | 福州大学 | A kind of road surface point cloud intensity enhancing method based on Vehicle-borne Laser Scanning data |
| CN109213763A (en) * | 2018-08-15 | 2019-01-15 | 武汉中海庭数据技术有限公司 | The organization and management method and system of Vehicle-borne Laser Scanning point cloud |
| CN109827703A (en) * | 2019-03-27 | 2019-05-31 | 歌尔股份有限公司 | Air pressure detection method, device and washing machine |
| CN110414577A (en) * | 2019-07-16 | 2019-11-05 | 电子科技大学 | A kind of laser radar point cloud multiple target Objects recognition method based on deep learning |
| CN110598541A (en) * | 2019-08-05 | 2019-12-20 | 香港理工大学深圳研究院 | Method and equipment for extracting road edge information |
| TWI695181B (en) * | 2018-11-13 | 2020-06-01 | 大陸商北京嘀嘀無限科技發展有限公司 | Methods and systems for color point cloud generation |
| CN111861946A (en) * | 2020-08-03 | 2020-10-30 | 中国科学院空天信息创新研究院 | Adaptive multi-scale vehicle-mounted laser radar dense point cloud data filtering method |
| CN111915662A (en) * | 2019-05-07 | 2020-11-10 | 北京京东尚科信息技术有限公司 | Three-dimensional laser point cloud data preprocessing method and device |
| CN112537302A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Driverless traffic vehicle lane keeping method and device and traffic vehicle |
| CN112986964A (en) * | 2021-02-26 | 2021-06-18 | 北京空间机电研究所 | Photon counting laser point cloud self-adaptive denoising method based on noise neighborhood density |
| CN113989784A (en) * | 2021-11-30 | 2022-01-28 | 福州大学 | Road scene type identification method and system based on vehicle-mounted laser point cloud |
| CN113989784B (en) * | 2021-11-30 | 2024-11-15 | 福州大学 | Road scene type identification method and system based on vehicle-mounted laser point cloud |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976467A (en) * | 2010-09-13 | 2011-02-16 | 天津市星际空间地理信息工程有限公司 | High-precision three-dimensional urban scene construction method integrating airborne LIDAR (Laser Intensity Direction And Ranging) technology and vehicle-mounted mobile laser scanning technology |
| CN102074047A (en) * | 2011-01-06 | 2011-05-25 | 天津市星际空间地理信息工程有限公司 | High-fineness urban three-dimensional modeling method |
| US20120281907A1 (en) * | 2011-05-06 | 2012-11-08 | Toyota Motor Engin. & Manufact. N.A.(TEMA) | Real-time 3d point cloud obstacle discriminator apparatus and associated methodology for training a classifier via bootstrapping |
-
2013
- 2013-07-19 CN CN201310307332.XA patent/CN103390169B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976467A (en) * | 2010-09-13 | 2011-02-16 | 天津市星际空间地理信息工程有限公司 | High-precision three-dimensional urban scene construction method integrating airborne LIDAR (Laser Intensity Direction And Ranging) technology and vehicle-mounted mobile laser scanning technology |
| CN102074047A (en) * | 2011-01-06 | 2011-05-25 | 天津市星际空间地理信息工程有限公司 | High-fineness urban three-dimensional modeling method |
| US20120281907A1 (en) * | 2011-05-06 | 2012-11-08 | Toyota Motor Engin. & Manufact. N.A.(TEMA) | Real-time 3d point cloud obstacle discriminator apparatus and associated methodology for training a classifier via bootstrapping |
Non-Patent Citations (1)
| Title |
|---|
| 谭贲 等: "车载激光扫描数据的地物分类方法", 《遥感学报》, vol. 16, no. 1, 31 January 2012 (2012-01-31), pages 50 - 57 * |
Cited By (42)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103675818B (en) * | 2013-12-03 | 2017-01-18 | 中国科学院深圳先进技术研究院 | Method and system for measuring polarimetric synthetic aperture radar data ground object three-dimensional distance |
| CN103675818A (en) * | 2013-12-03 | 2014-03-26 | 中国科学院深圳先进技术研究院 | Method and system for measuring polarimetric synthetic aperture radar data ground object three-dimensional distance |
| CN103778429A (en) * | 2014-01-24 | 2014-05-07 | 青岛秀山移动测量有限公司 | Method for automatically extracting road information in vehicle-mounted laser scanning point cloud |
| CN103778429B (en) * | 2014-01-24 | 2017-03-08 | 青岛秀山移动测量有限公司 | Automatic extraction method for road information in a kind of Vehicle-borne Laser Scanning point cloud |
| CN103743383A (en) * | 2014-02-11 | 2014-04-23 | 天津市星际空间地理信息工程有限公司 | Automatic extraction method for road information based on point cloud |
| CN103743383B (en) * | 2014-02-11 | 2016-09-07 | 星际空间（天津）科技发展有限公司 | A kind of Automatic extraction method for road information based on a cloud |
| CN103853840B (en) * | 2014-03-18 | 2017-05-03 | 中国矿业大学(北京) | Filter method of nonuniform unorganized-point cloud data |
| CN104463872A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Classification method based on vehicle-mounted LiDAR point cloud data |
| CN104463872B (en) * | 2014-12-10 | 2018-01-12 | 武汉大学 | Sorting technique based on vehicle-mounted LiDAR point cloud data |
| CN104596415A (en) * | 2014-12-29 | 2015-05-06 | 中国神华能源股份有限公司 | Method and device for determining lower edge of stack based on laser scanning single lines |
| CN104596415B (en) * | 2014-12-29 | 2017-06-09 | 中国神华能源股份有限公司 | A kind of method and apparatus that heap body lower edge is determined based on laser scanning single line |
| WO2017020466A1 (en) * | 2015-08-04 | 2017-02-09 | 百度在线网络技术（北京）有限公司 | Urban road recognition method, apparatus, storage medium and device based on laser point cloud |
| US10430659B2 (en) * | 2015-08-04 | 2019-10-01 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and apparatus for urban road recognition based on laser point cloud, storage medium, and device |
| KR102062680B1 (en) * | 2015-08-04 | 2020-01-06 | 바이두 온라인 네트웍 테크놀러지 (베이징) 캄파니 리미티드 | Laser point cloud based urban road recognition method, device, storage medium and device |
| CN105184852A (en) * | 2015-08-04 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Laser-point-cloud-based urban road identification method and apparatus |
| CN105184852B (en) * | 2015-08-04 | 2018-01-30 | 百度在线网络技术（北京）有限公司 | A kind of urban road recognition methods and device based on laser point cloud |
| CN106570446B (en) * | 2015-10-12 | 2019-02-01 | 腾讯科技（深圳）有限公司 | The method and apparatus of lane line drawing |
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN105550688B (en) * | 2015-12-04 | 2019-03-29 | 百度在线网络技术（北京）有限公司 | The classification method and device of point cloud data |
| CN105550688A (en) * | 2015-12-04 | 2016-05-04 | 百度在线网络技术（北京）有限公司 | Method and device for point cloud data classification |
| CN106127153A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning cloud data |
| CN106127153B (en) * | 2016-06-24 | 2019-03-05 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning point cloud data |
| CN108241819A (en) * | 2016-12-23 | 2018-07-03 | 高德软件有限公司 | The recognition methods of pavement markers and device |
| CN108241819B (en) * | 2016-12-23 | 2020-07-28 | 阿里巴巴（中国）有限公司 | Method and device for identifying pavement marker |
| CN108681525B (en) * | 2018-05-16 | 2020-12-25 | 福州大学 | Road surface point cloud intensity enhancing method based on vehicle-mounted laser scanning data |
| CN108681525A (en) * | 2018-05-16 | 2018-10-19 | 福州大学 | A kind of road surface point cloud intensity enhancing method based on Vehicle-borne Laser Scanning data |
| CN109213763B (en) * | 2018-08-15 | 2020-10-13 | 武汉中海庭数据技术有限公司 | Organization management method and system for vehicle-mounted laser scanning point cloud |
| CN109213763A (en) * | 2018-08-15 | 2019-01-15 | 武汉中海庭数据技术有限公司 | The organization and management method and system of Vehicle-borne Laser Scanning point cloud |
| US11474247B2 (en) | 2018-11-13 | 2022-10-18 | Beijing Didi Infinity Technology And Development Co., Ltd. | Methods and systems for color point cloud generation |
| TWI695181B (en) * | 2018-11-13 | 2020-06-01 | 大陸商北京嘀嘀無限科技發展有限公司 | Methods and systems for color point cloud generation |
| CN109827703A (en) * | 2019-03-27 | 2019-05-31 | 歌尔股份有限公司 | Air pressure detection method, device and washing machine |
| CN111915662A (en) * | 2019-05-07 | 2020-11-10 | 北京京东尚科信息技术有限公司 | Three-dimensional laser point cloud data preprocessing method and device |
| CN111915662B (en) * | 2019-05-07 | 2024-01-12 | 北京京东乾石科技有限公司 | Three-dimensional laser point cloud data preprocessing method and device |
| CN110414577A (en) * | 2019-07-16 | 2019-11-05 | 电子科技大学 | A kind of laser radar point cloud multiple target Objects recognition method based on deep learning |
| CN110598541A (en) * | 2019-08-05 | 2019-12-20 | 香港理工大学深圳研究院 | Method and equipment for extracting road edge information |
| CN111861946A (en) * | 2020-08-03 | 2020-10-30 | 中国科学院空天信息创新研究院 | Adaptive multi-scale vehicle-mounted laser radar dense point cloud data filtering method |
| CN111861946B (en) * | 2020-08-03 | 2023-03-28 | 中国科学院空天信息创新研究院 | Adaptive multi-scale vehicle-mounted laser radar dense point cloud data filtering method |
| CN112537302A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Driverless traffic vehicle lane keeping method and device and traffic vehicle |
| CN112986964A (en) * | 2021-02-26 | 2021-06-18 | 北京空间机电研究所 | Photon counting laser point cloud self-adaptive denoising method based on noise neighborhood density |
| CN112986964B (en) * | 2021-02-26 | 2023-03-31 | 北京空间机电研究所 | Photon counting laser point cloud self-adaptive denoising method based on noise neighborhood density |
| CN113989784A (en) * | 2021-11-30 | 2022-01-28 | 福州大学 | Road scene type identification method and system based on vehicle-mounted laser point cloud |
| CN113989784B (en) * | 2021-11-30 | 2024-11-15 | 福州大学 | Road scene type identification method and system based on vehicle-mounted laser point cloud |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103390169B (en) | 2016-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103390169A (en) | Sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects | |
| CN110264468B (en) | Point cloud data mark, parted pattern determination, object detection method and relevant device | |
| CN106022381B (en) | Automatic extraction method of street lamp pole based on vehicle-mounted laser scanning point cloud | |
| CN104463872B (en) | Sorting technique based on vehicle-mounted LiDAR point cloud data | |
| CN106918311A (en) | Isolated tree crown mapping area automatic calculating method based on vehicle-mounted laser cloud data | |
| CN102103202B (en) | Semi-supervised classification method for airborne laser radar data fusing images | |
| CN109633674A (en) | Three-dimensional Track automatic planning is maked an inspection tour in transmission of electricity based on laser point cloud data | |
| CN106204705A (en) | A kind of 3D point cloud segmentation method based on multi-line laser radar | |
| CN101702200A (en) | Automatic classification method of airborne laser radar point cloud data | |
| CN106599915B (en) | A kind of vehicle-mounted laser point cloud classifications method | |
| JP6347064B2 (en) | Laser measurement result analysis system | |
| CN114488194A (en) | Method for detecting and identifying targets under structured road of intelligent driving vehicle | |
| CN107918953B (en) | Three-dimensional space-based laser scanning power line point cloud extraction method and device | |
| CN109766824B (en) | Active and passive remote sensing data fusion classification method based on fuzzy evidence theory | |
| CN113487555B (en) | Point cloud meshing-based quick detection method for hidden danger points of power transmission line | |
| CN107832849A (en) | The power line gallery 3-D information fetching method and device in a kind of knowledge based storehouse | |
| Alburshaid et al. | Palm trees detection using the integration between gis and deep learning | |
| Li et al. | Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation | |
| CN114387293A (en) | Road edge detection method and device, electronic equipment and vehicle | |
| Wolf et al. | Techniques for Automated Classification and Segregation of Mobile Mapping 3D Point Clouds. | |
| CN117368118B (en) | Mining area biomass monitoring method based on multispectral and point cloud data processing | |
| KR101737889B1 (en) | filtering and extraction of feature boundary method from terrestrial lidar data using data mining techniques and device thereof | |
| CN115457496B (en) | Automatic driving retaining wall detection method and device and vehicle | |
| CN114399762B (en) | Road scene point cloud classification method and storage medium | |
| Wu et al. | Real-Time Point Cloud Clustering Algorithm Based on Roadside LiDAR |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160831 Termination date: 20190719 |